The Explicit Construction of Einstein Finsler Metrics with Non-Constant Flag Curvature

By using the Hawking Taub-NUT metric, this note gives an explicit construction of a 3-parameter family of Einstein Finsler metrics of non-constant flag curvature in terms of navigation representation

Autonomous Ground Vehicle

WildCat is an autonomous ground vehicle (AGV). AGVs were first developed for military purposes: Intelligent Transportation Systems (ITS), Manufacturing, Search and Rescue operations, Mining, etc. WildCat will be entered in the Intelligent Ground Vehicle competition (IGVC) held in June 2016 at Oakland University in Rochester, Michigan. Teams from major universities not only in the U.S., but also India, France, the UK, China, and around the world will be competing. The IGVC offers a design experience that is at the very cutting edge of engineering education. It is multidisciplinary, theory-based, hands-on, team implemented, and outcome assessed competition. It encompasses the very latest technologies impacting industrial development and taps subjects of high interest to students. The objective of the competition is to challenge students to think creatively as a team about the evolving technologies of vehicle electronic controls, sensors, computer science, robotics, and system integration throughout the design, fabrication, and field testing of autonomous intelligent mobile robots. The vehicle will compete to: 1) autonomously navigate an outdoor obstacle course as quickly as possible, keeping within the speed limit and reaching all GPS waypoints, 2) complete a course with remote (user) control, and 3) have ingenuity and uniqueness in design

Symmetry restoration and quantumness reestablishment

A realistic quantum many-body system, characterized by a generic microscopic Hamiltonian, is accessible only through approximation methods. The mean field theories, as the simplest practices of approximation methods, commonly serve as a powerful tool, but unfortunately often violate the symmetry of the Hamiltonian. The conventional BCS theory, as an excellent mean field approach, violates the particle number conservation and completely erases quantumness characterized by concurrence and quantum discord between different modes. We restore the symmetry by using the projected BCS theory and the exact numerical solution and find that the lost quantumness is synchronously reestablished. We show that while entanglement remains unchanged with the particle numbers, quantum discord behaves as an extensive quantity with respect to the system size. Surprisingly, discord is hardly dependent on the interaction strengths. The new feature of discord offers promising applications in modern quantum technologies.Comment: 17 pages and 3 figure

Hybrid Reinforcement Learning with Expert State Sequences

Existing imitation learning approaches often require that the complete demonstration data, including sequences of actions and states, are available. In this paper, we consider a more realistic and difficult scenario where a reinforcement learning agent only has access to the state sequences of an expert, while the expert actions are unobserved. We propose a novel tensor-based model to infer the unobserved actions of the expert state sequences. The policy of the agent is then optimized via a hybrid objective combining reinforcement learning and imitation learning. We evaluated our hybrid approach on an illustrative domain and Atari games. The empirical results show that (1) the agents are able to leverage state expert sequences to learn faster than pure reinforcement learning baselines, (2) our tensor-based action inference model is advantageous compared to standard deep neural networks in inferring expert actions, and (3) the hybrid policy optimization objective is robust against noise in expert state sequences.Comment: AAAI 2019; https://github.com/XiaoxiaoGuo/tensor4r

One-Shot Relational Learning for Knowledge Graphs

Knowledge graphs (KGs) are the key components of various natural language processing applications. To further expand KGs' coverage, previous studies on knowledge graph completion usually require a large number of training instances for each relation. However, we observe that long-tail relations are actually more common in KGs and those newly added relations often do not have many known triples for training. In this work, we aim at predicting new facts under a challenging setting where only one training instance is available. We propose a one-shot relational learning framework, which utilizes the knowledge extracted by embedding models and learns a matching metric by considering both the learned embeddings and one-hop graph structures. Empirically, our model yields considerable performance improvements over existing embedding models, and also eliminates the need of re-training the embedding models when dealing with newly added relations.Comment: EMNLP 201